Customer Segmentation for Marketing Analytics¶

In [1]:
# Import libraries 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
In [2]:
# Import data
df = pd.read_csv("Mall_Customers.csv")
df
Out[2]:
CustomerID Gender Age Annual Income (k$) Spending Score (1-100)
0 1 Male 19 15 39
1 2 Male 21 15 81
2 3 Female 20 16 6
3 4 Female 23 16 77
4 5 Female 31 17 40
... ... ... ... ... ...
195 196 Female 35 120 79
196 197 Female 45 126 28
197 198 Male 32 126 74
198 199 Male 32 137 18
199 200 Male 30 137 83

200 rows × 5 columns

In [3]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 5 columns):
 #   Column                  Non-Null Count  Dtype 
---  ------                  --------------  ----- 
 0   CustomerID              200 non-null    int64 
 1   Gender                  200 non-null    object
 2   Age                     200 non-null    int64 
 3   Annual Income (k$)      200 non-null    int64 
 4   Spending Score (1-100)  200 non-null    int64 
dtypes: int64(4), object(1)
memory usage: 7.9+ KB

Exploratory Data Analysis (EDA)¶

In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 5 columns):
 #   Column                  Non-Null Count  Dtype 
---  ------                  --------------  ----- 
 0   CustomerID              200 non-null    int64 
 1   Gender                  200 non-null    object
 2   Age                     200 non-null    int64 
 3   Annual Income (k$)      200 non-null    int64 
 4   Spending Score (1-100)  200 non-null    int64 
dtypes: int64(4), object(1)
memory usage: 7.9+ KB
In [5]:
df.isna().sum()
Out[5]:
CustomerID                0
Gender                    0
Age                       0
Annual Income (k$)        0
Spending Score (1-100)    0
dtype: int64
In [6]:
# Pairwise relationship between Variables of the Dataset
sns.pairplot(df)
plt.show()
In [7]:
numerical_df = df.select_dtypes(include=[np.number])
correlation = numerical_df.corr()
sns.heatmap(correlation, annot=True)
plt.title("Correlation Heatmap")
plt.show()
In [8]:
# Analyzing the data with respect to "Spending Score
sns.histplot(df['Spending Score (1-100)'], bins= 20, color='g')
plt.title("Distribution of Spending Score")
plt.show()
In [9]:
# Distribution of Spending Score with respect to Age
sns.displot(data= df,y='Spending Score (1-100)',x='Age')
plt.title("Distribution of Spending Score VS Age")
plt.show()
In [10]:
# Distribution of Spending Score with respect to Annual Income
sns.scatterplot(data= df,y='Spending Score (1-100)',x='Annual Income (k$)')
plt.title("Distribution of Spending Score VS Annual Income (k$)")
plt.show()
In [11]:
# Distribution of Annual Income
sns.histplot(df['Annual Income (k$)'], color='b')
plt.title("Distribution of Annual Income (k$)")
plt.show()
In [12]:
# Distributuion Annual Income with respect to Age
sns.displot(data= df,y='Annual Income (k$)',x='Age')
plt.title("Distribution of Annual Income (k$) VS Age ")
plt.show()
In [13]:
# Average Income distribution by Gender
gender_income = df.groupby('Gender')['Annual Income (k$)'].agg('mean').reset_index()
sns.barplot(data= gender_income, y='Gender', x='Annual Income (k$)')
plt.title("Gender VS Average Income")
plt.show()
In [14]:
df.describe()
Out[14]:
CustomerID Age Annual Income (k$) Spending Score (1-100)
count 200.000000 200.000000 200.000000 200.000000
mean 100.500000 38.850000 60.560000 50.200000
std 57.879185 13.969007 26.264721 25.823522
min 1.000000 18.000000 15.000000 1.000000
25% 50.750000 28.750000 41.500000 34.750000
50% 100.500000 36.000000 61.500000 50.000000
75% 150.250000 49.000000 78.000000 73.000000
max 200.000000 70.000000 137.000000 99.000000
In [15]:
df.select_dtypes("object").describe()
Out[15]:
Gender
count 200
unique 2
top Female
freq 112
In [16]:
# Gender Distribution
sns.countplot(data=df, x='Gender')
plt.title("Gender Distribution")
plt.show()
In [17]:
# Distribution of Annual Income & Age
plt.subplot(1,2,1)
sns.distplot(df['Annual Income (k$)'], color='b')
plt.title("Distribution of Annual Income")

plt.subplot(1,2,2)
sns.distplot(df['Age'], color='r')
plt.title("Distribution of Age")
plt.show()
In [18]:
# Gender VS Spending Score
sns.boxenplot(x=df['Gender'], y=df['Spending Score (1-100)'])
plt.title("Gender VS Spending Score")
plt.show()
In [19]:
# Age VS Annual Income & Spending Score
x = df['Age']
y = df['Annual Income (k$)']
z = df['Spending Score (1-100)']

sns.lineplot(data=df, x=x, y=y, color='purple', label="Annual Income")
sns.lineplot(data=df, x=x, y=z, color='c', label="Spending Score")
plt.title("Age VS Annual Income & Spending Score")
plt.legend()
plt.show()

Clustering¶

In [20]:
# Finding Clusters who relates to similar behaviour according to "Age, Annual Income & Spending Score"
X = df.iloc[:,2:]
X.shape
Out[20]:
(200, 3)

K-Means Clustering¶

In [21]:
# The Elbow method to find optimal number of Clusters
from sklearn.cluster import KMeans
Inter = []
for i in range(1,11):
  model = KMeans(n_clusters = i)
  model.fit(X)
  Inter.append(model.inertia_)

# plotting the Elbow
plt.plot(np.arange(1,11), Inter)
plt.xlabel('Clusters')
plt.ylabel('SSE')
plt.show()
In [22]:
# Fitting the model on optimum Clusters
# From the elbow curve it is clear that the optimum number of clusters i.e., n_clusters is 3.
K_model = KMeans(n_clusters = 3)
K_model.fit(X)
clusters = K_model.labels_
In [23]:
# Assigning the fitted Clusters to the X data to Visualize
X = X.assign(cluster = clusters)
X.sample(10)
Out[23]:
Age Annual Income (k$) Spending Score (1-100) cluster
129 38 71 75 0
47 27 40 47 1
48 29 40 42 1
60 70 46 56 1
53 59 43 60 1
170 40 87 13 2
19 35 23 98 1
73 60 50 56 1
147 32 77 74 0
99 20 61 49 1
In [24]:
import plotly.express as px
# Visualizing the Clusters
fig = px.scatter_3d(X, x='Age', y='Annual Income (k$)', z='Spending Score (1-100)',
              color='cluster', opacity=0.7)

# tight layout
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))